tfjs-models

Semantic Segmentation in the Browser: DeepLab v3 Model

This package contains a standalone implementation of the DeepLab inference pipeline, as well as a demo, for running semantic segmentation using TensorFlow.js.

DeepLab Demo

Usage

In the first step of semantic segmentation, an image is fed through a pre-trained model based on MobileNet-v2. Three types of pre-trained weights are available, trained on Pascal, Cityscapes and ADE20K datasets.

To get started, pick the model name from pascal, cityscapes and ade20k, and decide whether you want your model quantized to 1 or 2 bytes (set the quantizationBytes option to 4 if you want to disable quantization). Then, initialize the model as follows:

const tf = require('@tensorflow-models/tfjs');
const deeplab = require('@tensorflow-models/deeplab');
const loadModel = async () => {
  const modelName = 'pascal';   // set to your preferred model, either `pascal`, `cityscapes` or `ade20k`
  const quantizationBytes = 2;  // either 1, 2 or 4
  return await deeplab.load({base: modelName, quantizationBytes});
};

const input = tf.zeros([227, 500, 3]);
// ...

loadModel()
    .then((model) => model.segment(input))
    .then(
        ({legend}) =>
            console.log(`The predicted classes are ${JSON.stringify(legend)}`));

By default, calling load initalizes the PASCAL variant of the model quantized to 2 bytes.

If you would rather load custom weights, you can pass the URL in the config instead:

const deeplab = require('@tensorflow-models/deeplab');
const loadModel = async () => {
  const url = 'https://tfhub.dev/tensorflow/tfjs-model/deeplab/pascal/1/default/1/model.json?tfjs-format=file';
  return await deeplab.load({modelUrl: url});
};
loadModel().then(() => console.log(`Loaded the model successfully!`));

This will initialize and return the SemanticSegmentation model.

You can set the base attribute in the argument to pascal, cityscapes or ade20k to use the corresponding colormap and labelling scheme. Otherwise, you would have to provide those yourself during segmentation.

If you require more careful control over the initialization and behavior of the model (e.g. you want to use your own labelling scheme and colormap), use the SemanticSegmentation class, passing a pre-loaded GraphModel in the constructor:

const tfconv = require('@tensorflow/tfjs-converter');
const deeplab = require('@tensorflow-models/deeplab');
const loadModel = async () => {
  const base = 'pascal';        // set to your preferred model, out of `pascal`,
                                // `cityscapes` and `ade20k`
  const quantizationBytes = 2;  // either 1, 2 or 4
  // use the getURL utility function to get the URL to the pre-trained weights
  const modelUrl = deeplab.getURL(base, quantizationBytes);
  const rawModel = await tfconv.loadGraphModel(modelUrl);
  const modelName = 'pascal';  // set to your preferred model, out of `pascal`,
  // `cityscapes` and `ade20k`
  return new deeplab.SemanticSegmentation(rawModel);
};
loadModel().then(() => console.log(`Loaded the model successfully!`));

Use getColormap(base) and getLabels(base) utility function to fetch the default colormap and labelling scheme.

import {getLabels, getColormap} from '@tensorflow-models/deeplab';
const model = 'ade20k';
const colormap = getColormap(model);
const labels = getLabels(model);

Segmenting an Image

The segment method of the SemanticSegmentation object covers most use cases.

Each model recognises a different set of object classes in an image:

model.segment(image, config?) inputs

model.segment(image, config?) outputs

The output is a promise of a DeepLabOutput object, with four attributes:

model.segment(image, config?) example

const classify = async (image) => {
    return await model.segment(image);
}

Note: For more granular control, consider predict and toSegmentationImage methods described below.

Producing a Semantic Segmentation Map

To segment an arbitrary image and generate a two-dimensional tensor with class labels assigned to each cell of the grid overlayed on the image (with the maximum number of cells on the side fixed to 513), use the predict method of the SemanticSegmentation object.

model.predict(image) input

model.predict(image) output

model.predict(image) example

const getSemanticSegmentationMap = (image) => {
    return model.predict(image)
}

Translating a Segmentation Map into the Color-Labelled Image

To transform the segmentation map into a coloured image, use the toSegmentationImage method.

toSegmentationImage(colormap, labels, segmentationMap, canvas?) inputs

toSegmentationImage(colormap, labels, segmentationMap, canvas?) outputs

A promise resolving to the SegmentationData object that contains two attributes:

toSegmentationImage(colormap, labels, segmentationMap, canvas?) example

const base = 'pascal';
const translateSegmentationMap = async (segmentationMap) => {
  return await toSegmentationImage(
      getColormap(base), getLabels(base), segmentationMap)
}

Contributing to the Demo

Please see the demo documentation.

Technical Details

This model is based on the TensorFlow implementation of DeepLab v3. You might want to inspect the conversion script, or download original pre-trained weights here. To convert the weights locally, run the script as follows, replacing dist with the target directory:

./scripts/convert_deeplab.sh --target_dir ./scripts/dist

Run the usage helper to learn more about the options:

./scripts/convert_deeplab.sh -h
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